ε-Constrained Multiobjective Differential Evolution Using Linear Population Size Expansion

Research output: Journal PublicationsJournal Article (refereed)peer-review

3 Citations (Scopus)

Abstract

Constrained multiobjective optimization problems commonly arise in real-world applications. In the presence of constraints and multiple conflicting objectives, finding a set of feasible solutions which can best tradeoff different objectives is quite challenging. In this study, an ε-constrained multiobjective differential evolution using linear population size expansion is proposed to solve such a kind of problems. First, the ε-constraint-handling method, which originally solves constrained optimization problems with only one objective, is further improved to handle constraints in a multiobjective optimization way. Second, to achieve a better approximation to the feasible Pareto front, a linear population size expansion strategy is developed. Once enough feasible solutions have been found, the population size will be linearly increased to find more promising solutions. As a result, a simple yet efficient constrained multiobjective differential evolution is proposed. Experiments are conducted to evaluate the performance of the proposed algorithm on 35 benchmark test functions with different numbers of constraints and objectives. Obtained results are compared with seven state-of-the-art algorithms. Empirical results and comparisons demonstrate that our proposed algorithm achieves better or at least comparable performance to the competitors, and is capable of obtaining a set of representative feasible solutions for the selected real-world constrained multiobjective optimization problems, especially for highly constrained problems.

Original languageEnglish
Pages (from-to)445-464
Number of pages20
JournalInformation Sciences
Volume609
Early online date21 Jul 2022
DOIs
Publication statusPublished - Sept 2022

Bibliographical note

Funding Information:
This work is supported by the Academy of Finland (grant no. 250 122, 256 263, and 283 054).

Publisher Copyright:
© 2022 Elsevier Inc.

Keywords

  • Constrained multiobjective optimization
  • Differential evolution
  • Linear population size expansion
  • Real-world engineering applications
  • ε-Constrained-handling method

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